Quality of standardised patient research reports in the medical education literature: review and recommendations
Bibliographic record
Abstract
CONTEXT: In order to assess or replicate the research findings of published reports, authors must provide adequate and transparent descriptions of their methods. We conducted 2 consecutive studies, the first to define reporting standards relating to the use of standardised patients (SPs) in research, and the second to evaluate the current literature according to these standards. METHODS: Standards for reporting SPs in research were established by representatives of the Grants and Research Committee of the Association of Standardized Patient Educators (ASPE). An extensive literature search yielded 177 relevant English-language articles published between 1993 and 2005. Search terms included: 'standardised patient(s)'; 'simulated patient(s)'; 'objective structured clinical examination (OSCE)', and 'clinical skills assessment'. Articles were limited to those reporting the use of SPs as an outcome measure and published in 1 of 5 prominent health sciences education journals. Data regarding the SP encounter, SP characteristics, training and behavioural measure(s) were gathered. RESULTS: A random selection of 121 articles was evaluated according to 29 standards. Reviewers judged that few authors provided sufficient details regarding the encounter (21%, n = 25), SPs (16%, n = 19), training (15%, n = 15), and behavioural measures (38%, n = 44). Authors rarely reported SP gender (27%, n = 33) and age range (22%, n = 26), whether training was provided for the SPs (39%, n = 47) or other raters (24%, n = 29), and psychometric evidence to support the behavioural measure (23%, n = 25). CONCLUSIONS: The findings suggest that there is a need for increased rigor in reporting research involving SPs. In order to support the validity of research findings, journal editors, reviewers and authors are encouraged to provide adequate detail when describing SP methodology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.387 | 0.729 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.009 |
| Bibliometrics | 0.039 | 0.036 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".